Guides, cheat-sheets, and code libraries from my own SQL, Python, and machine learning prep — shared so you can learn from the same material. More resources (and a premium track) are on the way.
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A complete, beginner-friendly 4–6 month path into Data Analytics — no prior background needed. Skills, tools, timeline, portfolio ideas, and how to land your first role.
SQL, Python, statistics, machine learning, business case questions, and behavioral prep — everything to walk into a DS/DA interview ready.
Minimal, runnable Python code for every major supervised, unsupervised, and reinforcement learning model — Scikit-learn, PyTorch, Keras, and Gym.
How to actually read a HackerRank problem before touching code — input/output thinking, vocabulary, a worked example, and a reusable problem-solving template.
A step-by-step path from reading papers to finding a research gap, running experiments, and writing your own — for anyone starting out in ML/AI research.
A short set of beginner-to-advanced SQL questions covering joins, aggregation, window functions, and CTEs — good for a quick self-test.
The reusable checklist I run through before starting any new project — problem definition, research, scope, risk, and a final go/no-go check.
More guides are being built right now — here's what's coming next. You'll find them here as soon as they're ready.
A ready-to-adapt dashboard template and dataset walkthrough — the exact next step after the Career Transition Roadmap's visualization stage.
Pandas and NumPy patterns you'll actually reach for daily — cleaning, merging, grouping, and reshaping, in one quick-reference guide.
An expanded version of the practice set, built for daily drilling in the weeks before an interview.
New guides get added as I build them. Want first access, or something specific covered? Reach out.
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